AI Speech-to-Text Web App: Purpose and Prerequisites

In this guide, we'll explore the purpose and prerequisites for building an AI-powered speech-to-text web application that runs locally on your computer. This project leverages open-source AI technology to create a practical tool for transcribing spoken audio.

Project Purpose

The goal of this project is to create a web application that:

Captures audio from your computer's microphone
Processes that audio using OpenAI's Whisper model running locally
Returns accurate text transcriptions in real-time
Runs completely on your local machine (no data sent to external servers)
Provides a simple, user-friendly interface

This application allows you to transcribe spoken language into text without relying on cloud-based services, ensuring your data remains private and accessible even without an internet connection.

The Model: Whisper

For this project, we'll be using Whisper, an automatic speech recognition (ASR) system developed by OpenAI. Key features include:

Open-source and freely available
Trained on 680,000 hours of multilingual and multitask data
Supports transcription in multiple languages
Capable of translating speech into English
Can run efficiently on consumer hardware

We'll be using a smaller version of the model that balances accuracy with performance to ensure it runs well on standard hardware.

Technical Prerequisites

To complete this project, you should have:

Knowledge Prerequisites

Basic to intermediate Python programming skills
Familiarity with web concepts (HTML, CSS, JavaScript basics)
Understanding of virtual environments in Python
Basic command line/terminal skills

Hardware Prerequisites

Computer with at least 8GB RAM (16GB recommended)
At least 2GB of free disk space
A working microphone
Windows, macOS, or Linux operating system

Software Prerequisites

Python 3.8 or newer installed
Git (for downloading project files)
Internet connection (for initial setup only)

Development Tools

We'll be using the following technologies and libraries:

Python: Our primary programming language
Flask: A lightweight web framework for Python
Whisper: OpenAI's speech recognition model
PyAudio: For capturing audio from your microphone
AJAX/JavaScript: For making the web interface interactive
Bootstrap: For styling the web interface

Skill Level and Time Commitment

This project is appropriate for intermediate Python developers who have some experience with web development concepts. While we'll provide detailed explanations for each step, having a foundation in Python programming will make the process smoother.

Expected time to complete:

Setup and installation: 30-60 minutes
Development: 2-3 hours
Testing and refinement: 1 hour

By the end of this project, you'll have a functional speech-to-text web application that runs entirely on your local machine, providing accurate transcriptions without requiring an internet connection or sending your data to third-party servers.

In the next guide, we'll set up our development environment and install all necessary dependencies to get started building our application.

AI-Powered Speech-to-Text Web App